Zobrazeno 1 - 10
of 270
pro vyhledávání: '"Adrian Sandu"'
Publikováno v:
Tellus: Series A, Dynamic Meteorology and Oceanography, Vol 75, Iss 1, Pp 159–171-159–171 (2023)
The Ensemble Kalman Filters (EnKF) employ a Monte-Carlo approach to represent covariance information, and are affected by sampling errors in operational settings where the number of model realizations is much smaller than the model state dimension. T
Externí odkaz:
https://doaj.org/article/0dd65713dacd49bca8eeb0fb8996e374
Autor:
Haipeng Lin, Michael S. Long, Rolf Sander, Adrian Sandu, Robert M. Yantosca, Lucas A. Estrada, Lu Shen, Daniel J. Jacob
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 15, Iss 2, Pp n/a-n/a (2023)
Abstract Kinetic integration of large and stiff chemical mechanisms is a computational bottleneck in models of atmospheric chemistry. It requires implicit solution of the coupled system of kinetic differential equations with time‐consuming construc
Externí odkaz:
https://doaj.org/article/33220d8e27714487b5e7eff080cec00c
Multifidelity Ensemble Kalman Filtering Using Surrogate Models Defined by Theory-Guided Autoencoders
Autor:
Andrey A. Popov, Adrian Sandu
Publikováno v:
Frontiers in Applied Mathematics and Statistics, Vol 8 (2022)
Data assimilation is a Bayesian inference process that obtains an enhanced understanding of a physical system of interest by fusing information from an inexact physics-based model, and from noisy sparse observations of reality. The multifidelity ense
Externí odkaz:
https://doaj.org/article/fe684c0354044f25bd787f5818665127
Autor:
Adrian Sandu, Ahmed Attia
Publikováno v:
AIMS Geosciences, Vol 1, Iss 1, Pp 41-78 (2015)
Data assimilation combines information from models, measurements, and priors to obtain improved estimates of the state of a dynamical system such as the atmosphere. Ensemble-based data assimilation approaches such as the Ensemble Kalman filter (EnKF)
Externí odkaz:
https://doaj.org/article/45d452b9670043c5bcabeae80e926672
Autor:
Congmeng Lyu, Shannon L Capps, Amir Hakami, Shunliu Zhao, Jaroslav Resler, Gregory R Carmichael, Adrian Sandu, Armistead G Russell, Tianfeng Chai, Daven K Henze
Publikováno v:
Environmental Research Letters, Vol 14, Iss 12, p 124093 (2019)
Ground-level ozone, which forms photochemically in the atmosphere from precursor emissions of oxides of nitrogen (NO _x ) and volatile organic compounds, is a criteria pollutant that harms human health and public welfare. For a representative summer
Externí odkaz:
https://doaj.org/article/63732f46ef484258a45efc322c99f700
Publikováno v:
Atmosphere, Vol 2, Iss 3, Pp 510-532 (2011)
The solution of chemical kinetics is one of the most computationally intensivetasks in atmospheric chemical transport simulations. Due to the stiff nature of the system,implicit time stepping algorithms which repeatedly solve linear systems of equati
Externí odkaz:
https://doaj.org/article/16845bfcfd0a4983bc5868b7dc3c4309
Autor:
Tianfeng Chai, Adrian Sandu
Publikováno v:
Atmosphere, Vol 2, Iss 3, Pp 426-463 (2011)
Chemical data assimilation is the process by which models use measurements to produce an optimal representation of the chemical composition of the atmosphere. Leveraging advances in algorithms and increases in the available computational power, the i
Externí odkaz:
https://doaj.org/article/700c5bb1c1ff4e4b88420901af0c4824
Publikováno v:
Atmosphere, Vol 9, Iss 7, p 254 (2018)
This Special Issue presents efficient formulations and implementations of sequential and variational data assimilation methods. The methods address three important issues in the context of operational data assimilation: efficient implementation of lo
Externí odkaz:
https://doaj.org/article/ef96b2cf0ac04acfb5e11031d943071c
Publikováno v:
Atmosphere, Vol 9, Iss 6, p 213 (2018)
This paper presents a fully non-Gaussian filter for sequential data assimilation. The filter is named the “cluster sampling filter”, and works by directly sampling the posterior distribution following a Markov Chain Monte-Carlo (MCMC) approach, w
Externí odkaz:
https://doaj.org/article/924cd8c0fe5f49feb47a74c5270d45f8
Autor:
Azam Mooasvi, Adrian Sandu
Publikováno v:
Mathematical Modelling and Analysis, Vol 20, Iss 3 (2015)
This paper discusses new simulation algorithms for stochastic chemical kinetics that exploit the linearity of the chemical master equation and its matrix exponential exact solution. These algorithms make use of various approximations of the matrix ex
Externí odkaz:
https://doaj.org/article/3f7840e52c4446d08b1ceae56984ff9c